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Distinct Counts in Analysis Services 2005

About the Series ...

This
article is a member of the series Introduction to MSSQL Server Analysis
Services. The series is designed to provide hands-on application of
the fundamentals of MS SQL Server Analysis Services (Analysis
Services), with each installment progressively presenting features and
techniques designed to meet specific real-world needs. For more information on
the series, please see my initial article, Creating Our First Cube. For the software components, samples and tools
needed to complete the hands-on portions of this article, see Usage-Based Optimization in Analysis Services 2005, another article within this
series.

We have revisited distinct counts at points in other
articles within both my Introduction to MSSQL Server Analysis Services
and MDX Essentials series, examining specifics with regard to
appropriate use, and details of optimization within the perspective under
examination in the article concerned. In this article, we will introduce distinct
counts as they are managed in Analysis Services 2005. The redesign
of the capability based upon the hierarchy and attribute structure that debuts
in Analysis Services 2005 results in much more impressive performance
and flexibility of deployment within our integrated business intelligence
solutions, as many have come to report recently in blogs, forums, and other
media outlets.

In this article,
we will gain some hands-on exposure to distinct counts in Analysis
Services 2005. Our examination of the expanded capability will include:

A discussion
surrounding the general concepts of distinct counts, including why they
are useful (and often required) within the design of any robust analysis effort.

An examination
of some of the challenges inherent with using distinct counts in Analysis
Services 2000, and how distinct counts have been redesigned in Analysis
Services 2005 to overcome some of these shortcomings of the previous
version.

Creation of a
distinct count measure within a sample cube to demonstrate the ease with
which we can add distinct count capabilities to the cubes in our
individual business environments.

A discussion
of other considerations that surround the use of distinct counts.

Distinct Counts in Analysis Services 2005

Anyone working within the
realm of business intelligence and general analysis realizes, in short order,
that we often encounter the need to quantify precisely the members of
various sets of data. Those of us who have become familiar with Analysis
Services are aware of its capabilities, when it comes to categorizing and
aggregating data within the hierarchical contexts of dimensions and attributes.
We can, for the most part, readily tap these capabilities from the user
interface that Analysis Services provides. Through the exploitation of
more advanced approaches, including the use of calculated members / measures,
and multidimensional expressions (MDX) in general, we can extend our analysis
even further, and leverage Analysis Services to reach far more specific
objectives.

One of the basic
requirements that comes into play, at least in some form, in many analysis
scenarios, is the need to count the members of a set targeted for
analysis. An example might be the need to count the number of products we have
shipped from a given warehouse, or group of warehouses, to a given geographical
location, or to a specific group of stores. This can be accomplished readily
enough with the Count() function, as most of us are aware.

Count() does a great job of giving us a total
count. Of course, the results we would achieve in using Count()
with products, in the scenarios above, would represent total number of
products shipped. What we would not get, and what we might find far more
useful in some situations, would be a count of the different products
that were shipped. Count(), in providing a total number, would also be
providing multiple counts of the same products, because products will
have been shipped multiple times, in many instances. To reach our objective of
counting different products, then, we would need to count each
different product shipped, only once. To count them multiple times not
only misstates the number of different products, but it also likely
renders averages, and other metrics based upon the count value, meaningless or
misleading.

The word different here
is easily supplanted by distinct. Moreover, as many of us are aware, the
performance of distinct counts has historically presented a challenge in
the OLAP world. Lets introduce a simple example that illustrates the
challenge, and then transform that challenge to an opportunity to meet an
illustrative business need, using the newly expanded distinct count
capabilities found within Analysis Services 2005.